dd-l1-london-parks-count

Status: done
Score: 1.000
Duration: 0:34
Cost: 0.19¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

0:00
I've got inner-London parks (london_parks) and I'm sizing the corpus before commissioning a green-space accessibility study. How many of those parks come in at one hectare or more, what's their combined area in hectares, and what bounding box would I draw around that subset on a WGS84 map? Output parks_summary.json with keys count, total_area_ha, and bbox_wgs84 (as a [xmin, ymin, xmax, ymax] array).
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System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
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Bash ls -F 
london_parks.fgb
0:18
Write /work/solve.py
content · 52 lines · py 
import geopandas as gpd
import json

def main():
    # Load the parks
    parks = gpd.read_file('london_parks.fgb')
    
    # Assuming the geometry is in a projected CRS or we need to handle area calculation carefully.
    # Let's check the CRS first.
    print(f"Original CRS: {parks.crs}")
    
    # We need area in hectares. 1 hectare = 10,000 square meters.
    # To get accurate area, we should project to a metric CRS if not already.
    # EPSG:27700 (OSGB 1936 / British National Grid) is standard for UK.
    
    if parks.crs.is_geographic:
        parks_metric = parks.to_crs(epsg=27700)
    else:
        parks_metric = parks.copy()
        
    # Calculate area in square meters, then convert to hectares
    parks_metric['area_ha'] = parks_metric.geometry.area / 10000.0
    
    # Filter for parks >= 1 hectare
    large_parks = parks_metric[parks_metric['area_ha'] >= 1.0].copy()
    
    # Count
    count = len(large_parks)
    
    # Total area in hectares
    total_area_ha = large_parks['area_ha'].sum()
    
    # Bounding box in WGS84
    # Transform large_parks back to WGS84 for the bbox
    large_parks_wgs84 = large_parks.to_crs(epsg=4326)
    bounds = large_parks_wgs84.total_bounds # [xmin, ymin, xmax, ymax]
    
    # Prepare output
    output = {
        "count": int(count),
        "total_area_ha": float(total_area_ha),
        "bbox_wgs84": [float(b) for b in bounds]
    }
    
    with open('parks_summary.json', 'w') as f:
        json.dump(output, f, indent=4)
    
    print(f"Summary created: {output}")

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1586 chars).
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Bash python solve.py 
Original CRS: EPSG:27700
Summary created: {'count': 42, 'total_area_ha': 519.1620805430861, 'bbox_wgs84': [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]}
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Bash ls -F 
london_parks.fgb
parks_summary.json
solve.py
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Read /work/parks_summary.json 
     1	{
     2	    "count": 42,
     3	    "total_area_ha": 519.1620805430861,
     4	    "bbox_wgs84": [
     5	        -0.19932378751294652,
     6	        51.49147199538401,
     7	        -0.08248178747996572,
     8	        51.54706589542573
     9	    ]
    10	}
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